Our goal is to develop statistical models for the shape change of a configuration of “landmark” points (key points of interest) over time and to use these models for filtering and tracking to automatically extract landmarks, synthesis, and change detection. The term “shape activity” was introduced in recent work to denote a particular stochastic model for the dynamics of landmark shapes (dynamics after global translation, scale, and rotation effects are normalized for). In that work, only models for stationary shape sequences were proposed. But most “activities” of a set of landmarks, e.g., running, jumping, or crawling, have large shape changes with respect to initial shape and hence are nonstationary. The key contribution of this work is a novel approach to define a generative model for both 2D and 3D nonstationary landmark shape sequences. Greatly improved performance using the proposed models is demonstrated for sequentially filtering noise-corrupted landmark configurations to compute Minimum Mean Procrustes Square Error (MMPSE) estimates of the true shape and for tracking human activity videos, i.e., for using the filtering to predict the locations of the landmarks (body parts) and using this prediction for faster and more accurate landmarks extraction from the current image.